The chemical and pharmaceutical industries screen and develop hundreds of new chemicals and drug candidates each year. Chemists and engineers are tasked to develop process recipes for these new molecules and the recipes often involve multiple reaction steps coupled with separation steps such as crystallization or extraction. A critical consideration in the chemical process design is the choice of solvents and solvent mixtures, from among hundreds of solvent candidates, for reaction, separation, and purification (Frank, T. C. et al., “Quickly Screen Solvents for Organic Solids,” Chemical Engineering Progress, December 1999, 41). Phase behavior, especially solubility, of the new molecules in solvents or solvent mixtures weighs heavily in the choice of solvents for recipe development while little if any such experimental data is available for these new molecules. Although limited solubility experiments may be taken as part of the process research and development, during the early stages of development it is typical for limited experimental resource and drug substance availability, to restrict experimental program of solvent selection for process development. Where the process requires a mixed solvent system it is practically impossible to cover the full range of potential solvent combinations with sufficient detail to find the optimal solution, even with modern high throughput techniques. Consequently, solvent selection today is largely dictated by researchers' preferences or prior experiences. This often leads to a sub optimal manufacturing process reaching the pilot plant and potential manufacturability issues at various steps of the process. To overcome these obstacles, it is highly desirable to have a predictive model of chemical solubility in single and mixed solvent systems, based on a small initial set of measured solubilities.
Existing solubility parameter models such as that of Hansen (Hansen, C. M., Hansen Solubility Parameters: A User's Handbook; CRC Press, 2000) offer very limited predictive power while group contribution models such as UNIFAC (Fredenslund, A. et al., “Group-Contribution Estimation of Activity Coefficients in Nonideal Liquid Mixtures,” AIChE J. 21:1086, 1975) are rather inadequate due to missing functional groups or collapse of functional group additivity rule with large, complex molecules.
Recently Chen and Song (Chen, C.-C. and Y. Song, “Solubility Modeling with NonRandom Two-Liquid Segment Activity Coefficient Model,” Industrial and Engineering Chemistry Research, 43:8354, 2004a) and the related U.S. patent application Ser. No. 10/785,925, proposed a NonRandom Two-Liquid segment activity coefficient (NRTL-SAC) model for fast, qualitative correlation and estimation of solubility of organic nonelectrolytes in common solvents and solvent mixtures. Conceptually, the approach suggests that one could account for the liquid phase nonideality of mixtures of small solvent molecules and complex chemical molecules in terms of pre-defined conceptual segments with pre-determined binary interaction characteristics. Examples of the conceptual segments are hydrophobic segment, polar segment, and hydrophilic segment. The numbers of conceptual segments for each molecule, solvent or solute, reflect the characteristic surface interaction area and nature of the surface interactions. While loosely correlated with molecular structure, they are identified from true behavior of the molecules in solution, i.e., available experimental phase equilibrium data. The molecular make-up in terms of numbers of conceptual segments, i.e. hydrophobicity X, polarity types Y− and Y+, and hydrophilicity Z, constitutes the molecular parameters for the solvent and solute molecules. Given the molecular parameters for solvent and solute molecules, the model offers a thermodynamically consistent expression for estimation of phase behavior, including solubilities, for organic nonelectrolytes in chemical process design.